Discovering Latent Causes and Memory Modification: A Computational Approach Using Symmetry and Geometry

Arif Dönmez

Submission Track: Proceedings

Keywords: computational cognitive science, symmetry, geometry, algebra, algorithm, latent causes, memory modification, unsupervised learning, categorization, artificial intelligence

TL;DR: Latent Causes and Memory Modification by Symmetries

Abstract:

We learn from our experiences, even though they are never exactly the same. This implies
that we need to assess their similarity to apply what we have learned from one experience
to another. It is proposed that we “cluster” our experiences based
on hidden latent causes that we infer. It is also suggested that surprises, which occur
when our predictions are incorrect, help us categorize our experiences into distinct groups.
In this paper, we develop a computational theory that emulates these processes based on two basic concepts
from intuitive physics and Gestalt psychology using symmetry and geometry.
We apply our approach to simple tasks that involve inductive reasoning. Remarkably, the output of our computational approach aligns
closely with human responses.

Submission Number: 31